This paper considers a new method for the automatic generation of digital terrain models from LiDAR data. The method iterates a thin plate spline interpolated surface towards the ground, while pointsć residuals from the surface are inspected at each iteration, with a gradually decreasing window size. Top-hat transformation is used to enhance discontinuities caused by surface objects. Finally, parameter-free ground point filtering is achieved by automatic thresholding based on standard deviation. The experiments show that this method correctly determines DTM even in those cases of more difficult terrain features. The expected accuracy of ground point determination on those datasets commonly used in practice today is over 96%, while the average total error produced on the ISPRS benchmark dataset is under 6%.
COBISS.SI-ID: 15485718
Light Detection and Ranging (LIDAR) has become one of the prime technologies for rapid collection of vast spatial data, usually stored in a LAS file format (LIDAR data exchange format standard). In this article, a new method for lossless LIDAR LAS file compression is presented. The method applies three consequent steps: a predictive coding, a variable-length coding and an arithmetic coding. The key to the method is the prediction schema, where four different predictors are used: three predictors for x, y and z coordinates and a predictor for scalar values, associated with each LIDAR point. The method has been compared with the popular general-purpose methods and with a method developed specially for compressing LAS files. The proposed method turns out to be the most efficient in all test cases. On average, the LAS file is losslessly compressed to 12% of its original size.
COBISS.SI-ID: 14953494
In this paper a new method for high quality rendering of large LiDAR-based terrain data is presented. The visualization system upgrades previous methods of point-based rendering by detecting continuous surfaces and replacing them with decimated triangle meshes. High-quality visualization is retained by using render-to-texture methods to generate color textures and bump maps from original LiDAR data and applying them to the newly generated triangle meshes. This hybrid approach is able to decrease rendering times of surfaces to less than 50% with little to no difference in rendering quality. The described optimizations can be executed at run-time without interfering with user interaction.
COBISS.SI-ID: 26898727
A hardware accelerator for the compression of LIDAR data has been developed. For this purpose, hardware predictors of the point coordinates and other attributes of LIDAR data were conceived. The predictors of the point coordinates consist of two methods: linear prediction using last coordinate changes, and the search for the closest coordinate change among the most recent coordinate changes. The applied method is dynamically selected based on the resemblance of the current search result. A pipelined hardware divider, required for linear prediction, was also developed. An adjustable pipeline depth enabled us to select the most suitable divider with respect to the dividers’ latency, the usage of the hardware resources, and the clock period. The coordinate prediction and the prediction of other LIDAR data attributes are used in the prediction compression of the LIDAR data. Additionally, a variable length encoder was developed, and the arithmetic coder was improved by using the barrel shifter structure, which resulted in up to 8-times higher data throughput. Modules were developed in the VHDL language and verified in the Cadence simulation environment. Individual modules were synthesized and tested on the Xilinx XUPV5 prototype board.
COBISS.SI-ID: 26726695
The roof surfaces within urban areas are constantly attracting interest regarding the installation of photovoltaic systems. These systems can improve self-sufficiency of electricity supply, and can help to decrease the emissions of greenhouse gases throughout urban areas. Unfortunately, some roof surfaces are unsuitable for installing photovoltaic systems. This presented work deals with the rating of roofsurfaces within urban areas regarding their solarpotential and suitability for the installation of photovoltaic systems. The solarpotential of a roofćs surface is determined by a new method that combines extracted urban topography from LiDAR data with the pyranometer measurements of global and diffuse solar irradiances. Heuristic annual vegetation shadowing and a multi-resolution shadowing model, complete the proposed method. The significance of different influential factors (e.g. shadowing) was analysed extensively. A comparison between the results obtained by the proposed method and measurements performed on an actual PV power plant showed a correlation agreement of 97.4%.
COBISS.SI-ID: 16262934